Abstract #0601
Fast Reconstruction for Regularized Quantitative Susceptibility Mapping
Berkin Bilgic 1 , Audrey Fan 2 , Cornelius Eichner 1 , Stephen Cauley 1 , Jonathan Polimeni 1 , Marta Bianciardi 1 , Elfar Adalsteinsson 2 , Lawrence Wald 1 , and Kawin Setsompop 1
1
Martinos Center for Biomedical Imaging,
Charlestown, MA, United States,
2
MIT,
Cambridge, MA, United States
A high-resolution whole brain QSM reconstruction can
take up to 20 min on a workstation, which poses a limit
on QSM usability in clinical and research settings.
Herein, we introduce an improved Split-Bregman (SB)
L1-regularized dipole inversion algorithm that offers
20 faster reconstruction relative to the standard
nonlinear conjugate gradient (NCG) solver. Additionally,
we extend SB L1-regularization to admit
magnitude-weighting that prevents smoothing across edges
identified on the magnitude signal, and solve this more
complicated problem 5 faster than the NCG approach.
Further, we extend the previously proposed closed-form
L2-based inversion to admit magnitude-weighting, and
demonstrate 15 acceleration relative to NCG by
employing a preconditioner that leads to faster
convergence. Utility of the proposed methods is
demonstrated in high-resolution (0.6 mm isotropic) 3D
GRE data at 3T, as well as multi-echo Simultaneous
Multi-Slice (SMS) EPI time-series at 7T.
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